Using the table furnished by Kontxi Martinez, Samples IDs are replaced by the experiment name in the read courn table. The experiment name follow the current pattern:
The corrected read count table is imported. Only value of the BRAUN pair are kept. The homemade function LoadRawReadCounts() is used for this.
Read count are normalized using the ReadCountNormalisation() homemade function
The first step consist in removing features, here genes, with no or low reads in all samples. The sequencing detection limit is estimated at 10. This means that genes with less than 10 reads in all samples are considered as not expressed (or expressed under the detection limit) and are thus removed from the analyis. Here, 0 genes have been rejected. The filtering is based on the the filterByExpr from the edgeR packgage. The figure shows the density of gene read counts. Note the vertical line indicating the threshold used to sort out none expressed genes.
Barplot shows the whole number of read counts for each sample. Reads which are not associated to an annotated gene, are not considered in the calculation.
A filtering is also applied to identify and remove genes with less than 10 reads (see the Low read filtering tab). Reads associated to these filtered out genes are rejected.